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• Genomic Profiling of Sequentially Acquired Metastatic Sites from an "Exceptional Responder" Lung Adenocarcinoma Patient Reveals Extensive Genomic Heterogeneity and Novel Somatic Variants

  We employed next-generation sequencing to identify somatic alterations in multiple metastatic sites from an "exceptional responder" lung adenocarcinoma patient during his seven year course of ERBB2-directed therapies. The degree of heterogeneity was unprecedented, with ~1% similarity between somatic alterations of the lung and lymph nodes. One novel translocation, PLAG1-ACTA2, present in both sites, up-regulated ACTA2 expression. ERBB2, the predominant driver oncogene, was amplified in both sites, more pronounced in the lung, and harbored an L869R mutation in the lymph node. Functional studies demonstrated increased proliferation, migration, metastasis, and resistance to ERBB2-directed therapy due to L869R mutation and increased migration due to ACTA2 overexpression. Within the lung, a nonfunctional CDK12, due to a novel G879V mutation, correlated with down-regulation of DNA damage response genes, causing genomic instability, and sensitivity to chemotherapy. We propose a model whereby a sub-clone metastasized early from the primary site and evolved independently in lymph nodes.

  Study phs001159

• Epigenetic and metabolomic data from type 2 diabetes adolescents

  Objective: Rates of type 2 diabetes (T2D) among adolescents are on the rise. Epigenetic changes could be associated with the metabolic alterations in adolescents with T2D. Methods: We performed a cross-sectional integrated analysis of DNA methylation data from peripheral blood mononuclear cells with serum metabolomic data from First Nation adolescents with T2D and controls participating in the Improving Renal Complications in Adolescents with type 2 diabetes through REsearch (iCARE) cohort study, to explore the molecular changes in adolescents with T2D. Results: Our analysis showed that 43 serum metabolites and 36 differentially methylated regions (DMR) were associated with T2D. Several DMRs were located near the transcriptional start site of genes with established roles in metabolic disease and associated with altered serum metabolites (e.g. glucose, leucine, and gamma-glutamylisoleucine). These included the free fatty acid receptor-1 (FFAR1), upstream transcription factor-2 (USF2), and tumor necrosis factor-related protein-9 (C1QTNF9), among others. Conclusions: We identified DMRs and metabolites that merit further investigation to determine their significance in controlling gene expression and metabolism which could define T2D risk in adolescents.

  Study EGAS00001003816

• WES in HCCOs with varying Doxorubicin resistance

  We aimed to identify somatic mutations that could explain the resistance to Doxorubicin. Therefor, we purified Exomes from liver biopsies and HCC organoids using Agilent SureSelectXT (clinical research exome, HS2 V8, or Human all Exon V6 Cosmic) and performed WES. Matched non-tumoral liver biopsies served as the control for germline mutations. 12 of these samples were previously reported. Sequence reads were aligned to the reference human genome GRCh38 using Burrows-Wheeler Aligner (BWA, v0.7.17). Duplicate removal, base quality adjustment, mutation calling (Mutect2, intervals: SureSelectXT_HS_V8_S33266340, germline-recourse: gnomad.exomes.r2.1.1.sites.liftover_grch38, Panel-of-Normal: 1000g_pon.hg38), filtering of Mutect calls, and functional annotation (Funcotator, funcotator_dataSources.v1.7.20200521g) were performed using the Genome Analysis Toolkit (GATK, v4.4) according to the Broad Institute best practices in Somatic short variant discovery workflows. Mutations with a functional annotation of Deletion, Insertion, Non-stop, Splice-Site, Start-Codon and Missense mutations were kept. A list of 43 commonly mutated genes in HCC was analyzed in the HCC organoids [TCGA, Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma. Cell, 2017. 169(7): p. 1327-1341.e23].

  Study EGAS50000000043

• An Isogenic Cell Line Panel for Sequence-based Screening of Targeted Anti-cancer Drugs

  We describe the creation and characterization of an isogenic cell line panel representing common cancer pathways, with multiple features optimized for high-throughput screening. More than 1,800 cell lines from three normal human cells were generated using CRISPR-technologies. Surprisingly, we discovered most of these lines did not result in complete gene inactivation, despite integration of sgRNA at the desired genomic site. However, a subset of the lines harbored true, biallelic disruptions of the targeted tumor suppressor gene, yielding a final panel of 100 well-characterize lines covering 19 pathways frequently subject to loss of function in cancers. This panel included genetic markers optimized for sequence-based ratiometric assays for drug-based screening assays. To illustrate the potential utility of this panel, we developed a multiplexed high-throughput screen that identified Wee1 inhibitor MK-1775 as a selective growth inhibitor of cells with inactivation of TP53. These cell lines and screening approach should prove useful for researchers studying a variety of cellular and biochemical phenomena

  Study EGAS00001005974

• San Francisco Bay Area Latina Breast Cancer Study

  The genome-wide association study (GWAS) includes participants enrolled into two different studies. The first study, the San Francisco Bay Area Cancer Study (SFBCS) is a population-based case-control study of breast cancer conducted in the San Francisco Bay Area and included women ages 35-79 years from three racial/ethnic groups: Non-Hispanic whites, African Americans, and Hispanics/Latinas. For the GWAS, only Hispanic/Latina women were included. Women diagnosed with invasive breast cancer between 1995 and 2002 were identified through the Greater Bay Area Cancer Registry. Controls were identified by random digit dialing and were frequency-matched to cases by age in 5 year increments and by race/ethnicity. Hispanic/Latina ethnicity was assessed by self-report. 175 Hispanic/Latina cases and 307 Hispanic/Latina controls from the SFBCS had given adequate consent and provided biospecimens that were used in the GWAS to be included in this data submission. The second study is the Northern California site of the Breast Cancer Family Registry (NC-BCFR). This population-based family study recruited breast cancer cases ages 18-64 years diagnosed from 1995-2009 that were identified through the Greater Bay Area Cancer Registry. Cases included all women at increased genetic susceptibility for breast cancer who met one or more of the following criteria: (a) being diagnosed with breast cancer at age <35 years; b) having a personal history of ovarian cancer or childhood cancer; (c) being diagnosed with two different breast cancers (bilateral breast cancers), with the first one diagnosed at age <50 years; and d) having one or more first-degree relatives with breast cancer, ovarian cancer or childhood cancer. Cases not meeting these criteria were randomly sampled and racial/ethnic minorities were oversampled. Controls were recruited by random digit dialing and were matched by 5-year age increments and by race/ethnicity. For the current GWAS only Latina/Hispanic cases and controls were included. Latina/Hispanic ethnicity was assessed by self-report. 631 Hispanic/Latina cases and 61 Hispanic/Latina controls from the NC-BCFR had given adequate consent and provided biospecimens that were used in the GWAS to be included in this data submission.

  Study phs000912

• Losartan Effects on Emphysema Progression (LEEP-BioLINCC)

  Data Access NOTE: Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions. Objectives To evaluate the efficacy of losartan, an angiotensin receptor blocker, to reduce emphysema progression in patients with COPD and mild to moderate emphysema. Background Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease comprising chronic bronchitis and emphysema. COPD is a major cause of morbidity and mortality. Smoking cessation slows the progression of the disease, and bronchodilators can provide sustained improvement in lung function, but there are no pharmacologic agents that clearly modify emphysema progression. Angiotensin receptor blockers (ARBs) have been suggested as potential drugs to slow the progression of COPD. A few trials have shown that patients treated with ARBs had slower progression of radiographic emphysema. The LEEP study was initiated to test the hypothesis that the ARB losartan would reduce the progression of emphysema in patients with COPD with mild to moderate emphysema.ParticipantsA total of 220 participants were enrolled. 108 participants were randomized to receive losartan, and 112 participants were randomized to receive the placebo.Design LEEP was a randomized, placebo-controlled multicenter trial. Participants were randomly assigned (1:1) to receive losartan or placebo. The dose was 50 mg losartan or placebo given as one capsule daily for 2 weeks, and, if well tolerated and the systolic blood pressure was >90 mm Hg, it was increased to two capsules. Participants and site investigators were masked to treatment assignment. Participant responses to the St George's Respiratory Questionnaire, the modified Medical Research Council dyspnea scale, the COPD Assessment Test, and the Physical Function–Short Form 20a were collected. The number and severity of COPD exacerbations were recorded. COPD exacerbations were defined as two or more new or worse symptoms for ≥3 days and classified by treatment as mild (neither antibiotics nor oral steroids), moderate (an antibiotic or oral steroid), or severe (hospitalization). The primary outcome measure was the change in quantitative whole-lung emphysema score after 48 weeks measured by the percentage of lung voxels less than -950 Hounsfield units (PCT950) on full inspiratory HRCT. Conclusions Losartan does not prevent progression of emphysema in patients with COPD who have mild to moderate pulmonary emphysema.

  Study phs004313

• Whole Exome Sequencing of Congenital Diaphragmatic Hernia Patients and Trios

  Congenital diaphragmatic hernia (CDH) is a common and severe birth defect characterized by structural defects of the diaphragm and by pulmonary hypoplasia. Congenital diaphragmatic hernia patient may present either as an isolated phenotype or together with other congenital anomalies in a complex phenotype. Despite the clinical significance of CDH, the underlying genetic and developmental pathways are incompletely understood. In order to establish a catalog of human genetic variation for this condition, we performed whole exome sequencing (WES) on 275 carefully phenotyped individuals with CDH in the Pediatric Surgical Research Laboratories at the Massachusetts General Hospital (Boston, MA, USA) and Boston Children's Hospital (Boston, MA, USA). The exome data generated are valuable for comparison of candidate genes derived from WES of other CDH cohorts or affected kindreds, and to provide ideal candidates for further functional studies with the ultimate goal of enhancing our understanding of the heterogeneous and, possibly, oligogenic molecular etiology of CDH. While familial clustering has been reported in rare kindreds, the majority of probands with CDH have no family history of CDH leading to the hypothesis that de novo variants are an important and relatively frequent etiological mechanism. In the second version of the study, we performed WES analysis on 87 trios, to assess the contribution of de novo mutations in the etiology of diaphragmatic and pulmonary defects and to identify previously unknown candidate genes. This dbGaP submission includes WES data on: (a) 20 new probands, (b) 9 probands also reported in the previous version (dbGaP accession no. phs000783.v1.p1), (c) 174 unaffected parents, including the parents of 67 previously reported probands. Combined analysis with other available cohorts of congenital diaphragmatic hernia revealed a genome-wide enrichment of likely gene-disrupting de novo variants (i.e., nonsense, frameshift or splice site), and missense de novo variants predicted in silico to be damaging.

  Study phs000783

• Single-nucleus RNA-sequencing data of kidney biopsies from patients with primary FSGS, maladaptive FSGS, proteinuric controls and healthy controls

  This dataset includes FASTQ files of single-nucleus RNA sequencing of cryopreserved kidney biopsy cores from adult patients with diagnosed primary FSGS (n = 9, all nephrotic), maladaptive FSGS (n = 9, not nephrotic), proteinuric controls (PLA2R-positive membranous nephropathy, n = 3), and healthy controls (n = 4). A total of 120,751 high-quality nuclei were identified, including 2,471 podocytes and 1,574 parietal epithelial cells (PECs). In addition to the raw FASTQ files, the dataset includes processed data files from all 25 samples, generated using Seurat in R: barcode files, features, count matrices, and associated metadata. Details regarding the bioinformatics pipeline can be found at https://github.com/lambrechtslab/FSGS_Deleersnijder_et_al

  Dataset EGAD50000001557

• FEGA and European GDI: working together to improve human health

  The Federated EGA is a global resource for discovery of and access to sensitive human omics and associated data consented for secondary use, through a network of human data repositories to accelerate biomedical research and improve human health. The Federated EGA network was launched in September 2022 with five inaugural nodes, and since 2023 seven operational nodes can share data across national borders in adherence to European and national laws. A few weeks after FEGA's official launch, in November 2022, the European Genomic Data Infrastructure (GDI) project was kicked-off . This European Commission co-funded project, coordinated by ELIXIR, is aimed to deliver federated, sustainable and secure data infrastructure to access genomic and related phenotypic and clinical data across Europe. This project supports the aim of the 1+MG initiative (25 EU countries, Norway and UK) to enable personalised medicine and health through a shared framework and infrastructure for securely accessing and integrating high quality genomic data and other health data across borders.1+MG will be an integral component of the European Health Data Space (EHDS) for secondary use (Healthdata@EU) as an authorised participant. How are Federated EGA and GDI similar? Given their shared visions, the Federated EGA and European GDI networks have a lot in common. To begin, they share the same overall goal of establishing networks of “nodes” that host sensitive human data within a jurisdiction and connecting these nodes in a global network to support data discovery and promote human genome data access for research and healthcare. FEGA and GDI are both initially focused on enabling access to human genomic data, while FEGA ambition is to expand the scope to clinical research data and other omics data in the future. FEGA and GDI are both built on open and interoperable software solutions, a subset of which are based on the LocalEGA components. FEGA and GDI implementation solutions are based on international community standards, for example those developed by the Global Alliance for Genomics and Health, which contributes to making them interoperable. Both FEGA and GDI allow for the nodes to make use of any solution that is fully compatible. As illustrated in Figure 1, a final key point is the significant overlap of institutions involved in envisioning and operating FEGA and GDI nodes, with the clear wish to keep them interoperable in the future. What distinguishes Federated EGA from GDI? Despite being largely similar, there are some differences between the FEGA and GDI networks, which we aim to clarify in this post. The first difference is the governance model that the nodes will operate to comply with national laws and GDPR. In the FEGA network, nodes have taken inspiration from the EGA data access model, where the infrastructure is a data processor of the hosted data and data controllership remains with the originating Data Access Committees (i.e. Data controllers) for each dataset. On the other hand, in the GDI network the controllership of datasets will be transferred to a 1+MG European Digital Infrastructure Consortium (EDIC) legal entity created by the Member States (MS), who will make data access decisions, with data holders having veto powers for their datasets. Importantly, FEGA nodes have the flexibility to choose another model to fit with their data protection framework, including becoming data controllers for their datasets. The second difference is the inclusion criteria for data. While FEGA nodes are designed to accept almost any type of omics data in need of control access (e.g. genomics, transcriptomics, genotyping, single cells sequencing, patient-tracked metagenomics), GDI nodes are initially, but not only, focused on accepting whole genome and exome sequencing data and affiliated data from sources such as (i) the Genome of Europe use case of the 1+MG initiative which specifically aims to fulfil the mission of building “a European network of national genomic reference cohorts of at least 500,000 citizens”) (ii) data collection of other types of genomic data identified by countries through the 1+MG dashboard and (iii) genomic data coming from data holders which would need to fulfil EHDS requirements. The third difference is the maturity of the software stacks. The FEGA network provides a set of software for data and metadata submission, storage, permissions management, and file distribution (the LocalEGA package). GDI is building a complete set of open source reference for the five functionalities covering the full data life cycle (a few more compared to the LocalEGA) including federated processing (analytics, AI/ML), which is still under active development. Notably, one of the GDI functionalities - storage and interfaces - can be satisfied by using the LocalEGA storage solution, highlighting the ability of FEGA and GDI to be interoperable. In both FEGA and GDI, nodes are allowed to use alternative solutions as long as they are fully interoperable with the networks. Figure 1 provides a simple overview of these described commonalities and differences. Figure1: schematic overview of the main commonalities and difference among a FEGA and a GDI node Can the same institute run a FEGA and a GDI node at the same time? The answer is Yes! We believe this is entirely possible and encouraged. As long as 1+MG requirements are continuously fulfilled in the implementation. The same trained personnel could operate the infrastructure and leverage the same national funding to run a FEGA and a GDI node. Several European nodes, especially GDI vanguard nodes like Norway, Sweden, Finland and Spain are following this model. The idea is that a lot of the work can be reused, given the overlapping scope and the interoperable technology. Thus, the hosted datasets can be discovered via the two catalogues, perhaps accessible under the same or different governance models. From a FEGA node perspective, the GDI datasets could simply be a subset of the datasets hosted in the FEGA node, which have their specific governance as any other. What's next? So, the amazing teams of people building the Federated EGA network and the European GDI network are working together to create infrastructures able to provide secure access to human genomic and associated data around Europe. Because we all know how much human genomics can improve healthcare and precision medicine. And we all want to collaborate to make it happen. This article has been reviewed collectively by members of the EGA and the GDI coordination team.

  Blog fega-and-gdi

• National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS)

  NIAGADS is an NIH controlled-access data repository that collaborates with researchers around the world to facilitate data access and sharing for genomic and associated omics data related to Alzheimer's Disease (AD), Alzheimer's Disease Related Dementias (ADRD), as well as healthy aging. Serving as the data coordinating center for the Alzheimer's Disease Sequencing Project (ADSP), NIAGADS hosts all ADSP-generated whole-exome, whole-genome and associated phenotypes. Other data types hosted by NIAGADS include genotype SNP arrays, functional omics, fluid biomarkers, aggregated imaging data (such as volumetric data for brain regions of interest), and summary statistics. In adherence with NIH security best practices and genome data sharing policies, NIAGADS supports data access requests through review by an NIH-appointed and registered Data Access Committee. Data stored within NIAGADS cannot be requested via dbGaP. Data requests must go through the NIAGADS Data Sharing Service (DSS) via the following NIAGADS url: https://dss.niagads.org/. Questions related to the request of data should be directed to the NIAGADS admin team at: niagads@pennmedicine.upenn.edu. DSS is a FISMA certified platform built by a team of data scientists at the University of Pennsylvania to support the request and access of AD/ADRD data by qualified researchers internationally. Questions related to the use of data hosted in the DSS should be directed to the Portal admin team at: help@niagads.org.

  Study phs004180